Instructions to use ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient")
model = AutoModelForCausalLM.from_pretrained("ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient

SGLang

How to use ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient with Docker Model Runner:
```
docker model run hf.co/ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lumi-tess

This model was created with the goal for a good llama 3 uncencored model with long context. At it worked like a charm.

Did a merge with breadcrumbs_ties method. Instruct gradient, Lumimaid and Tess.

Uses llama 3 context

Sampler wise it has a very wide optimal so works with lots of different settings.

Thanks to the people who train the custom models: Undi IkariDev For Lumimaid.

Migel Tissera for Tess

base_model: [] library_name: transformers tags:

mergekit
merge

model

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base.

Models Merged

The following models were included in the merge:

E:\Llama-3-Lumimaid-70B-v0.1-OAS
I:\Tess-2.0-Llama-3-70B-v0.2

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: I:\Llama-3-70B-Instruct-Gradient-262k
    parameters:
      weight: 0.20
      density: 0.90
      gamma: 0.01
  - model: I:\Tess-2.0-Llama-3-70B-v0.2
    parameters:
      weight: 0.20
      density: 0.90
      gamma: 0.01
  - model: E:\Llama-3-Lumimaid-70B-v0.1-OAS
    parameters:
      weight: 0.60
      density: 0.90
      gamma: 0.01
merge_method: breadcrumbs_ties
base_model: I:\Llama-3-70B-Instruct-Gradient-262k
dtype: bfloat16

My followup model, that improves in all aspects can be found at: https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6

Downloads last month: 15

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for ryzen88/Llama-3-70b-Uncensored-Lumi-Tess-gradient

Quantizations

3 models